Congratulations to the Grid team on the fundraise and the announcement! Exciting stuff.
It seems like there is an emerging consensus that (a) DL development requires access to massive compute, but (b) if you’re only using off-the-shelf PyTorch or TensorFlow, moving your model from your personal development environment to a cluster or cloud setting is too difficult — it is easy to spend most of your time managing infrastructure rather than developing models. At Determined AI, we’ve spent the last few years building an open source DL training platform that tries to make that process a lot simpler (https://github.com/determined-ai/determined), but I think it's fair to say that this is still very much an open space and an important problem. Curious to take a look at Grid AI and see how it compares to other tools in the space -- some other alternatives include Kubeflow, Polyaxon, and Spell AI.
Hi William -- we have absolutely not copied any of Lightning's APIs.
In fact, our PyTorch API makes some significantly different design choices than Lightning does -- e.g., we require users to step optimizers and run the backward pass explicitly, which is a bit lower-level but allows for more flexibility when using the API.
Projects copying from each other is exactly in the open source spirit. Don't release something with an Apache License if you don't want people copying it with compatible licenses. Also, patents of open source software (to limit uses that the license would otherwise allow) and API copyrights are pretty strongly frowned upon in the open source community. As a note, the Apache license you released Lightning under grants patent lawsuit protection to anyone using your code under the license so claiming copyright and patent infringement on another Apache licensed project seems amazingly skeevy.
If this is the philosophical stance that Grid and Lightning are taking then it's definitely a project I'm going to advice people to stay well clear off. It's the worst flavor of commercialized open source software and potentially a legal liability to touch in any way as you seem way too lawsuit trigger happy.
The Apache License you released Lightning under explicitly allows people to clone your framework if they adhere to the license and provides them patent protection if they do so.
The Apache License you released Lightning under explicitly allows people to clone your framework if they adhere to the license and provides them patent protection if they do so.
As I said in my previous comment, using patents to try and get around an open source license is skeevy as hell.
It seems like there is an emerging consensus that (a) DL development requires access to massive compute, but (b) if you’re only using off-the-shelf PyTorch or TensorFlow, moving your model from your personal development environment to a cluster or cloud setting is too difficult — it is easy to spend most of your time managing infrastructure rather than developing models. At Determined AI, we’ve spent the last few years building an open source DL training platform that tries to make that process a lot simpler (https://github.com/determined-ai/determined), but I think it's fair to say that this is still very much an open space and an important problem. Curious to take a look at Grid AI and see how it compares to other tools in the space -- some other alternatives include Kubeflow, Polyaxon, and Spell AI.